# Pytorch (MNIST)
### Overview
This example shows a complete step in the pipeline through custom endpoints:
1. Pre-processing step
2. Serve a model step
3. Post-processing step
### Adding package to the classpath
We need to add the main package to the classpath so that the notebook can load all the necessary libraries from Konduit-Serving into the Jupyter Notebook kernel.
```
%classpath add jar ../../konduit.jar
```
{% hint style="info" %}
Classpaths can be considered similar to `site-packages` in the python ecosystem where each library that's to be imported to your code is loaded from.
{% endhint %}
### Viewing the configuration file
To view the configuration file contents, use the following command and select the JSON file you want to view.
```
%%bash
less config.json
```
You'll be able to view the following.
```
{
"host" : "localhost",
"port" : 0,
"protocol" : "HTTP",
"pipeline" : {
"steps" : [ {
"@type" : "IMAGE_TO_NDARRAY",
"config" : {
"height" : 28,
"width" : 28,
"dataType" : "FLOAT",
"includeMinibatchDim" : true,
"aspectRatioHandling" : "CENTER_CROP",
"format" : "CHANNELS_FIRST",
"channelLayout" : "GRAYSCALE",
"normalization" : {
"type" : "SCALE"
},
"listHandling" : "NONE"
},
"keys" : [ "image" ],
"outputNames" : [ "Input3" ],
"keepOtherValues" : true,
"metadata" : false,
"metadataKey" : "@ImageToNDArrayStepMetadata"
}, {
"@type" : "LOGGING",
"logLevel" : "INFO",
"log" : "KEYS_AND_VALUES"
}, {
"@type" : "ONNX",
"modelUri" : "mnist.onnx",
"inputNames" : [ "Input3" ],
"outputNames" : [ "Plus214_Output_0" ]
}, {
"@type" : "CLASSIFIER_OUTPUT",
"inputName" : "Plus214_Output_0",
"labels" : [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ],
"allProbabilities" : false
} ]
}
}
```
### Starting a server
Starts a server in the background with an id of `onnx-mnist` using `config.json` as configuration file without creating the manifest jar file before launching the server.
```
%%bash
konduit serve -id onnx-mnist -c config.json -rwm -b
```
You'll get the following message once the server starts in the background.
```
Starting konduit server...
Using classpath: /root/konduit/bin/../konduit.jar
INFO: Running command /root/miniconda/jre/bin/java -Dkonduit.logs.file.path=/root/.konduit-serving/command_logs/onnx-mnist.log -Dlogback.configurationFile=/tmp/logback-run_command_2ead2d4d1b15431d.xml -jar /root/konduit/bin/../konduit.jar run --instances 1 -s inference -c config.json -Dserving.id=onnx-mnist
For server status, execute: 'konduit list'
For logs, execute: 'konduit logs onnx-mnist'
```
We use `konduit list` command to view the list of activated server
```
%%bash
konduit list
```
The list of the activated server is below.
```
Listing konduit servers...
# | ID | TYPE | URL | PID | STATUS
1 | onnx-mnist | inference | localhost:33895 | 888 | started
```
To view the logs of the running server, use `konduit logs` commands.
```
%%bash
konduit logs onnx-mnist --lines 100
```
The output of server logging.
```
14:59:45.188 [main] INFO a.k.s.c.l.command.KonduitRunCommand - Processing configuration: /root/konduit/demos/2-pytorch-onnx-mnist/config.json
.
.
.
####################################################################
# #
# | / _ \ \ | _ \ | | _ _| __ __| | / | / #
# . < ( | . | | | | | | | . < . < #
# _|\_\ \___/ _|\_| ___/ \__/ ___| _| _|\_\ _) _|\_\ _) #
# #
####################################################################
14:59:45.601 [vert.x-worker-thread-0] INFO a.k.s.v.verticle.InferenceVerticle - Pending server start, please wait...
.
.
.
14:59:45.733 [vert.x-eventloop-thread-0] INFO a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server is listening on host: 'localhost'
14:59:45.733 [vert.x-eventloop-thread-0] INFO a.k.s.v.p.h.v.InferenceVerticleHttp - Inference HTTP server started on port 33895 with 4 pipeline steps
```
### Making a prediction
Let's display the test image before feeding it as an input into the model for the classification.
```
%%html
```
Here is the test image used in the prediction for this example:
`konduit predict` command is used to classify the image based on the model served in Konduit-Serving with the id given before.
```
%%bash
konduit predict onnx-mnist --input-type multipart 'image=@test-image.jpg'
```
You'll be able to get the output similar to the following.
```
{
"Plus214_Output_0" : [ [ -1.7924803, -9.652266, 11.478509, 5.148998, -7.9367347, 9.756878, 0.544513, -7.6820283, 9.234719, -6.431969 ] ],
"prob" : 11.478508949279785,
"index" : 2,
"label" : "2"
}
```
### Stopping the server
After we're finished with the server, we can terminate it through the `konduit stop` command.
```
%%bash
konduit stop onnx-mnist
```
You'll receive this message once the server is terminated.
```
Stopping konduit server 'onnx-mnist'
Application 'onnx-mnist' terminated with status 0
```
---
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```
GET https://serving.konduit.ai/examples/ipython-notebook/onnx/onnx-pytorch-mnist.md?ask=
```
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The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.